仅数μL血液或可同期识别12种平常癌症！基于Olink技术的泛癌血液蛋白质组分析初步结果颁布
<span style="color: black;"><strong style="color: blue;"><span style="color: black;">导读</span></strong></span><p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><span style="color: black;">癌症<span style="color: black;">精细</span>医学旨在利用基因组学、蛋白质组学和代谢组学等分子<span style="color: black;">工具</span>，实现高分辨率的个性化诊断，并对癌症<span style="color: black;">病人</span>进行个性化治疗和监测。其中，对癌症<span style="color: black;">病人</span>的血液蛋白质组进行全面表征有助于更好地<span style="color: black;">认识</span><span style="color: black;">疾患</span>的病因，能够更早地对<span style="color: black;">病人</span>进行诊断、<span style="color: black;">危害</span>分层，更好地监测治疗效果。</span></p>
<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><span style="color: black;"><span style="color: black;">日前</span>，适合肿瘤早筛的生物标志物被广泛<span style="color: black;">科研</span>并逐步应用于临床。但大<span style="color: black;">都数</span>人群筛查项目的假阳性率相对较高，这对<span style="color: black;">病人</span>造<span style="color: black;">成为了</span>不必要的心理<span style="color: black;">包袱</span>。<span style="color: black;">另外</span>，<span style="color: black;">日前</span>针对不同类型癌症筛查的项目仍然缺乏，<span style="color: black;">因此呢</span>急需一种单一简便的筛查<span style="color: black;">方法</span>，<span style="color: black;">能够</span>在<span style="color: black;">初期</span><span style="color: black;">发掘</span>不同类型的癌症。</span></p><span style="color: black;"><span style="color: black;">近期</span>，瑞典皇家理工学院等单位的<span style="color: black;">科研</span>人员在<strong style="color: blue;">Research Square</strong>预印本<span style="color: black;">发布</span>了题为“Next generation pan-cancer blood proteome profiling using proximity extension assay”<span style="color: black;">文案</span>，<strong style="color: blue;"><span style="color: black;">仔细</span>描述了一种用于泛癌分析的新策略：<span style="color: black;">经过</span>比较不同类型癌症<span style="color: black;">病人</span>的<span style="color: black;">血液</span>蛋白组图谱，找到每种类型癌症的特异性标记，并用以区分不同癌症类型</strong>。</span><span style="color: black;"><span style="color: black;">科研</span>团队分析了来自标准化生物样本库的1,400多名癌症<span style="color: black;">病人</span>样本以及丰富的临床数据，<span style="color: black;">包含</span>结直肠癌、肺癌等最<span style="color: black;">平常</span>的<span style="color: black;">12种癌症类型</span>。初步<span style="color: black;">科研</span>结果<span style="color: black;">显示</span>，<strong style="color: blue;">该<span style="color: black;">办法</span><span style="color: black;">仅<span style="color: black;">运用</span>数μL血液就能对数千种蛋白质进行定量分析，用于</span>区分癌症<span style="color: black;">病人</span>与健康个体，以在<span style="color: black;">疾患</span><span style="color: black;">初期</span><span style="color: black;">周期</span>进行诊断。</strong></span>
<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><img src="https://mmbiz.qpic.cn/mmbiz_png/DMKW2dzPflIJbjPm5icDY4f1W64IbtiaCVibD0u45mXYQ6cIoIJmAtQZse3ZBo0cNP2wVxYrIWFfA6BibXIiatPYibzg/640?wx_fmt=png&tp=webp&wxfrom=5&wx_lazy=1&wx_co=1" style="width: 50%; margin-bottom: 20px;"></p>
<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><span style="color: black;"><span style="color: black;">文案</span><span style="color: black;">发布</span>在Research Square</span></p>
<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><strong style="color: blue;"><span style="color: black;">重点</span><span style="color: black;">科研</span>内容</strong></p>
<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><span style="color: black;"><span style="color: black;">科研</span>团队<span style="color: black;">运用</span>Olink Explore 1536邻位延伸分析技术（PEA）对1,477名癌症<span style="color: black;">病人</span>和74名健康个体的<span style="color: black;">血液</span>蛋白质组进行了表征，<strong style="color: blue;"><span style="color: black;">运用</span><3μL<span style="color: black;">血液</span>对1,463种蛋白质进行定量。</strong>为识别每种癌症的<span style="color: black;">血液</span>蛋白组特征，<span style="color: black;">科研</span>团队<strong style="color: blue;">设计了一个基于人工智能预测模型和差异蛋白表达分析的工作流程</strong>。<span style="color: black;">首要</span>，将所有被分析的蛋白质结果用作<span style="color: black;">疾患</span><span style="color: black;">结果</span>的预测因子，以确定反映<span style="color: black;">每一个</span>癌症样本<span style="color: black;">疾患</span>状态的蛋白质；接下来，<span style="color: black;">经过</span>差异表达分析<span style="color: black;">选取</span>在一种癌症类型中与其他癌症类型相比上调的蛋白质子集。</span></p><strong style="color: blue;"><span style="color: black;">结合<span style="color: black;">以上</span>两个结果，<span style="color: black;">科研</span>团队为每种癌症类型<span style="color: black;">选取</span>了一组<span style="color: black;">关联</span>的上调蛋白，并<span style="color: black;">科研</span>了基于所选蛋白质的多<span style="color: black;">归类</span>模型能否精确区分<span style="color: black;">病人</span>的癌症类型。</span></strong><span style="color: black;"><span style="color: black;">经过</span><span style="color: black;">创立</span>癌症预测模型，对健康队列中的每种癌症进行<span style="color: black;">归类</span>，进一步验证了所选生物标志物的<span style="color: black;">潜能</span>，并<strong style="color: blue;"><span style="color: black;">最后</span>证实这种<span style="color: black;">归类</span><span style="color: black;">能够</span>准确识别<span style="color: black;">初期</span>癌症<span style="color: black;">病人</span></strong>。</span>
<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><img src="https://mmbiz.qpic.cn/mmbiz_png/DMKW2dzPflIJbjPm5icDY4f1W64IbtiaCVDsRMu0tgdzE0QMRohfAhiac9TxaMtfAfk4iaZhDnjrgm3Pd7CVZCicY5A/640?wx_fmt=png&tp=webp&wxfrom=5&wx_lazy=1&wx_co=1" style="width: 50%; margin-bottom: 20px;"></p>
<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><span style="color: black;">图1. 整体<span style="color: black;">科研</span>策略。<span style="color: black;">源自</span>：Research Square</span></p>
<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><span style="color: black;"><strong style="color: blue;"><span style="color: black;">癌症特异性蛋白的鉴定</span></strong></span></p><span style="color: black;">基于1,477名<span style="color: black;">病人</span>1,463个蛋白质靶点的<span style="color: black;">血液</span>蛋白图谱，<span style="color: black;">科研</span>团队生<span style="color: black;">成为了</span><span style="color: black;">表率</span>12种癌症类型个人<span style="color: black;">血液</span>蛋白水平的200多万个数据点，<span style="color: black;">重点</span>目的是确定每一种癌症的蛋白质特征，以<span style="color: black;">帮忙</span>进行泛癌症识别。初步分析结果<span style="color: black;">表示</span>，<strong style="color: blue;">在特定的癌症类型中存在几个上调和下调的蛋白质，</strong>其中<span style="color: black;">有些</span>潜在的生物标志物是癌症特异性的，如急性髓系白血病中的Fms<span style="color: black;">关联</span>受体酪氨酸激酶3（FLT3）和骨髓瘤中的SLAM家族成员7（SLAMF7），另<span style="color: black;">有些</span>则在两种或两种以上的癌症中被<span style="color: black;">发掘</span><span style="color: black;">上升</span>。有趣的是，<strong style="color: blue;">在所有四种与免疫细胞<span style="color: black;">关联</span>的癌症中，B淋巴细胞抗原受体CD79b分子（CD79b）的<span style="color: black;">血液</span>水平<span style="color: black;">上升</span></strong>。</span>
<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><img src="https://mmbiz.qpic.cn/mmbiz_png/DMKW2dzPflIJbjPm5icDY4f1W64IbtiaCViaZcGgVnO79WI4eTviaB2so096J2DJ8wvnTzfsEdKEqEp6yJWWhWue1A/640?wx_fmt=png&tp=webp&wxfrom=5&wx_lazy=1&wx_co=1" style="width: 50%; margin-bottom: 20px;"></p><span style="color: black;">图2. 癌症特异性蛋白的鉴定。<span style="color: black;">源自</span>：Research Square</span>
<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><span style="color: black;"><strong style="color: blue;"><span style="color: black;">模型构建及性能<span style="color: black;">评定</span></span></strong></span></p><span style="color: black;">在<span style="color: black;">以上</span>检测结果的<span style="color: black;">基本</span>上，为鉴定出一组与所<span style="color: black;">科研</span>癌症<span style="color: black;">关联</span>的蛋白质，<span style="color: black;">科研</span>团队分别<span style="color: black;">运用</span>所有定量的蛋白质（n = 1,463）和70%的癌症<span style="color: black;">病人</span><span style="color: black;">做为</span>训练集，为每种癌症类型（n = 12）构建了基于人工智能的<span style="color: black;">疾患</span>预测模型。<span style="color: black;">最后</span>，<strong style="color: blue;"><span style="color: black;">科研</span>团队<span style="color: black;">创立</span>了一个基于83个上调蛋白集的预测模型来<span style="color: black;">评定</span>泛癌样本<span style="color: black;">归类</span>的准确性，<span style="color: black;">发掘</span></strong><strong style="color: blue;">每种癌症都<span style="color: black;">拥有</span>不同的<span style="color: black;">血液</span>蛋白组图谱</strong>。</span><span style="color: black;"><strong style="color: blue;">与仅<span style="color: black;">运用</span>每种癌症最<span style="color: black;">明显</span>的蛋白质标记物（n = 12）相比，<span style="color: black;">运用</span>蛋白质组（n = 83）的预测性能有了巨大的<span style="color: black;">提高</span></strong>。这<span style="color: black;">亦</span>证明了<span style="color: black;">运用</span><span style="color: black;">血液</span>蛋白质组的额外<span style="color: black;">优良</span>，例如乳腺癌<span style="color: black;">病人</span>的单个标记物<span style="color: black;">无</span><span style="color: black;">选取</span>性，但<span style="color: black;">运用</span>多种蛋白质的预测模型产生了更准确的<span style="color: black;">归类</span>。</span>
<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><img src="https://mmbiz.qpic.cn/mmbiz_png/DMKW2dzPflIJbjPm5icDY4f1W64IbtiaCVuDFcmL2WsXuKq8v4qOGgeibmKq5oQxYAUVBoZEvWTSGFr6XfeH3SUOA/640?wx_fmt=png&tp=webp&wxfrom=5&wx_lazy=1&wx_co=1" style="width: 50%; margin-bottom: 20px;"></p><span style="color: black;">图3. 区分健康与<span style="color: black;">疾患</span><span style="color: black;">病人</span>的性能<span style="color: black;">评定</span>。<span style="color: black;">源自</span>：Research Square</span><span style="color: black;">接下来，为<span style="color: black;">评定</span><span style="color: black;">血液</span>蛋白质组区分<span style="color: black;">初期</span>癌症<span style="color: black;">病人</span>与健康个体的能力，<span style="color: black;">科研</span>团队将重点放在<span style="color: black;">初期</span>结直肠癌和肺癌<span style="color: black;">病人</span>身上，在这些<span style="color: black;">病人</span>中，<span style="color: black;">疾患</span>晚期的样本量相对<span style="color: black;">很强</span>。结果<span style="color: black;">表示</span>，<strong style="color: blue;"><span style="color: black;"><span style="color: black;">血液</span>蛋白质组</span>区分I期肺癌<span style="color: black;">病人</span>和健康个体的AUC为0.79；区分I期结直肠癌<span style="color: black;">病人</span>和健康对照的AUC为0.78</strong>。<span style="color: black;">以上</span>结果<span style="color: black;">显示</span>，这些蛋白质标志物在识别<span style="color: black;">初期</span>结直肠癌和肺癌<span style="color: black;">病人</span>方面的<span style="color: black;">潜能</span>，但仍需在独立队列中进行更深入的分析、验证。<strong style="color: blue;"><span style="color: black;">运用</span><span style="color: black;">以上</span><span style="color: black;">血液</span>蛋白质组能够以高<span style="color: black;">敏锐</span>性和特异性对大<span style="color: black;">都数</span>癌症类型的<span style="color: black;">血液</span>样本进行分层，还能够检测出<span style="color: black;">初期</span><span style="color: black;">疾患</span>患</strong><strong style="color: blue;">者</strong>。</span>
<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><img src="https://mmbiz.qpic.cn/mmbiz_png/DMKW2dzPflIJbjPm5icDY4f1W64IbtiaCV6AMZOlxCVFYHhqN8sia2NiaaAXcOqg359sfek4LuDJKTkjfZ29l4e7MQ/640?wx_fmt=png&tp=webp&wxfrom=5&wx_lazy=1&wx_co=1" style="width: 50%; margin-bottom: 20px;"></p>
<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><span style="color: black;">图4. <span style="color: black;">初期</span><span style="color: black;">疾患</span><span style="color: black;">病人</span>检测性能的<span style="color: black;">评定</span>。<span style="color: black;">源自</span>：Research Square</span></p>
<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><strong style="color: blue;">结语</strong></p><span style="color: black;">综上所述，<span style="color: black;">科研</span>团队描述了一种基于下一代<span style="color: black;">血液</span>分析的新策略，即仅<span style="color: black;">运用</span>数μL血液<span style="color: black;">就可</span><span style="color: black;">同期</span>识别12种<span style="color: black;">平常</span>癌症，使<span style="color: black;">科研</span>人员能够探索大<span style="color: black;">都数</span><span style="color: black;">重点</span>癌症类型中单个蛋白质的概况。该检测<span style="color: black;">办法</span>为<span style="color: black;">运用</span>一组蛋白质在单一检测中识别大<span style="color: black;">都数</span><span style="color: black;">平常</span>癌症的低成本泛癌症人群诊断开辟了可能性。<strong style="color: blue;">这种策略<span style="color: black;">能够</span>更早地<span style="color: black;">发掘</span>癌症，从而<span style="color: black;">帮忙</span>临床<span style="color: black;">大夫</span>在更早的<span style="color: black;">周期</span><span style="color: black;">起始</span>治疗癌症<span style="color: black;">病人</span>。</strong></span><strong style="color: blue;"><span style="color: black;">该<span style="color: black;">科研</span><span style="color: black;">触及</span>到的数据资源被整合在人类<span style="color: black;">疾患</span>血液图谱（Human Disease Blood Atlas）中。</span></strong><span style="color: black;">该图谱是人类蛋白质图谱（The Human Protein Atlas, HPA）项目的一部分。HPA于2003年<span style="color: black;">起步</span>，由瑞典几家<span style="color: black;">科研</span><span style="color: black;">公司</span>运营，旨在绘制细胞、组织和器官中的所有人类蛋白质图谱。<span style="color: black;">日前</span>，HPA<span style="color: black;">包括</span>来自40多种人体组织类型的蛋白质表达数据，涵盖超过15,000种基因产物，约占预测的人类蛋白质组的80%。</span><span style="color: black;"><span style="color: black;">文案</span>通讯作者、HPA计划负责人Mathias Uhlen教授<span style="color: black;">暗示</span>：“这个名为‘人类<span style="color: black;">疾患</span>血液图谱’的项目旨在<span style="color: black;">科研</span>100多种不同<span style="color: black;">疾患</span>，每年对大约1万人进行分析。该项目转向基于血液的<span style="color: black;">疾患</span>概况，部分<span style="color: black;">原由</span>是近年来用于检测血液中<span style="color: black;">海量</span>蛋白质的高灵敏度、高通量技术的发展。在分析血液中的蛋白质方面，有<span style="color: black;">有些</span>惊人的新技术。这项新技术<span style="color: black;">能够</span>用于<span style="color: black;">非常多</span><span style="color: black;">科研</span>，我认为这是血液分析的一个范式转变。”</span>
<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><span style="color: black;">参考资料：</span></p>
<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><span style="color: black;">1. Mathias Uhlen, María Bueno Álvez, Fredrik Edfors et al. Next generation pan-cancer blood proteome profiling using proximity extension assay, 01 November 2022, PREPRINT (Version 1) available at Research Square .</span></p>
<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><span style="color: black;">2. https://www.genomeweb.com/proteomics-protein-research/human-protein-atlas-using-olink-tech-move-plasma-proteomic-profiling#.Y472gsjwqvM.</span></p><strong style="color: blue;">·END ·</strong>
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<p style="font-size: 16px; color: black; line-height: 40px; text-align: left; margin-bottom: 15px;"><span style="color: black;"><span style="color: black;">爱好</span>就点个“在看”吧</span></p>
“NB”（牛×的缩写，表示叹为观止） 感谢楼主分享，祝愿外链论坛越办越好！ 你说得对，我们一起加油，未来可期。 你的留言真是温暖如春，让我感受到了无尽的支持与鼓励。
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